The Impact of Accelerometer and Heart Rate Data on Hypoglycemia Mitigation in Type 1 Diabetes

Abstract

Background: Aerobic exercise can lower blood glucose levels and alter insulin sensitivity both during and several hours after exercise, creating challenges for a closed-loop artificial pancreas. Predictive low glucose suspend (PLGS) algorithms are a first step toward an artificial pancreas, but few of these have been successfully applied to exercise. This study incorporates physical activity measurements from a combined accelerometer/heart rate monitor (HRM) to improve the performance of an existing PLGS algorithm at mitigating exercise-associated hypoglycemia in participants with type 1 diabetes. Methods: In all, 22 subjects with type 1 diabetes on insulin pump therapy were provided a combined accelerometer/HRM and (if not already using one) a continuous glucose monitor (CGM), then instructed to go about their everyday lives while wearing the devices. After the monitoring period, each subject’s insulin pump, CGM, and accelerometer/HRM were downloaded and the data were used to augment an existing PLGS algorithm to incorporate activity. Using a computer simulator, the accelerometer-augmented algorithm was compared to the HRM-augmented algorithm to determine which was most effective at mitigating hypoglycemia. Results: Mean length of monitoring was 4.9 days. Across all subjects, 11 061 CGM readings were recorded during the monitoring period. In the simulator analysis, the PLGS algorithm reduced hypoglycemia by 62%, compared to 71% and 74% reductions for the HRM-augmented and accelerometer-augmented algorithms, respectively; combined accelerometer and HRM augmentation provided a 76% reduction. Conclusions: In a simulated setting, the accelerometer-augmented pump suspension algorithm decreases the incidence of exercise-related hypoglycemia by a meaningful amount compared to the PLGS algorithm alone. Results also failed to justify the additional user burden of a HRM.